Another notable concern is the sensitivity of dimensionality reduction methods to noise [1]}. This problem is particularly apparent in the application of multidimensional scaling to our metabolic trait dataset. Visible in the embedding is a strong sensitivity to outliers (Figs. S3A-S3C), leading to a tight cluster of genomes with very little visible structure. Within this cluster, important features are generally difficult to resolve.
